Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jpantsjoha/ai-native-developer-experience --skill alibaba-expertgit clone --depth 1 https://github.com/jpantsjoha/ai-native-developer-experienceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jpantsjoha/ai-native-developer-experience/alibaba-expert)<a href="https://agentmods.dev/skills/jpantsjoha/ai-native-developer-experience/alibaba-expert"><img src="https://agentmods.dev/badge/skills/jpantsjoha/ai-native-developer-experience/alibaba-expert.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00064 | $0.00954 |
| Opus 5 | $0.00032 | $0.00477 |
| Sonnet 5 | $0.00013 | $0.00191 |
| Haiku 4.5 | $0.00006 | $0.00095 |
Grade A, and why
alibaba-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Alibaba Cloud Expert
Same discipline, different control plane: RAM and ActionTrail are the guardrails; Model Studio and AgentScope are the agent surface. The mainland/international split is a design input, not an afterthought.
This skill enforces the discipline that makes Alibaba Cloud workloads production-safe: identity, data boundaries, cost controls, and residency. It is not a feature tour — it is a checklist of the things that cause incidents and compliance failures when skipped.
When to use
- Designing any Alibaba Cloud infrastructure (new or modified)
- Before deploying agents or LLM workloads (Model Studio / Bailian, DashScope, PAI, AgentScope)
- When reviewing a Terraform plan or architecture for an Alibaba Cloud workload
- When a system touches regulated data or spans mainland-China and international regions
Procedure
-
Identity and RAM — verify least-privilege for every principal:
- RAM roles with STS temporary credentials over long-lived AccessKey pairs. No AccessKeys in code or config.
- Policies scoped to the specific function; resource groups used to bound blast radius.
- ActionTrail enabled on all account activity and shipped to a central audit store.
-
Data boundaries — for every data store:
- Classification recorded (public / internal / confidential / regulated — note PIPL and Data Security Law categories where in scope).
- OSS bucket policies explicit; public access blocked; KMS CMK where required.
- Cross-account sharing explicit and documented.
-
Data residency — for each resource:
- Mainland-China vs international accounts are separate control planes — decide which side the workload and its data live on; regulated mainland data stays in mainland regions.
- For Model Studio / DashScope calls: confirm the endpoint region matches the residency requirement.
-
Cost controls — for every LLM, compute, or storage resource:
- Budget alerts configured (User Center budgets) at staged thresholds.
- DashScope token quotas and rate limits set; unbounded agent loops are unbounded spend.
- Auto-scaling maximums bounded.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 72 lines · 64 tokens per session scan A 516a55377634
alibaba-expert is a skill published in the GitHub repository jpantsjoha/ai-native-developer-experience (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 954 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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